scholarly journals Orbital precession modulates interannual rainfall variability, as recorded in an Early Pleistocene speleothem

Geology ◽  
2018 ◽  
Vol 46 (8) ◽  
pp. 731-734 ◽  
Author(s):  
Philip J. Hopley ◽  
Graham P. Weedon ◽  
Chris M. Brierley ◽  
Christopher Thrasivoulou ◽  
Andy I.R. Herries ◽  
...  
Keyword(s):  
South Africa ◽  
Interannual Variability ◽  
Climate Model ◽  
Rainfall Variability ◽  
Early Pleistocene ◽  
Stone Tool ◽  
Climate Projections ◽  
African Rainfall ◽  
Interannual Rainfall Variability ◽  
Instrumental Records

Abstract Interannual variability of African rainfall impacts local and global communities, but its past behavior and response in future climate projections are poorly understood. This is primarily due to short instrumental records and a lack of long high-resolution palaeoclimate proxy records. Here we present an annually resolved 91,000 year Early Pleistocene record of hydroclimate from the early hominin-bearing Makapansgat Valley, South Africa. Changes in speleothem annual band thickness are dominated by precession over four consecutive orbital cycles with strong millennial-scale periodicity. The frequency of interannual variability (2.0–6.5 yr oscillations) does not change systematically, yet its amplitude is modulated by the orbital forcing. These long-term characteristics of interannual variability are reproduced with transient climate model simulations of water balance for South Africa from the Late Pleistocene to Recent. Based on these results, we suggest that the frequency of interannual variations in southern African rainfall is likely to be stable under anthropogenic warming, but that the size of year-to-year variations may increase. We see an orbitally forced increase in the amplitude of interannual climate variability between 1.8 Ma and 1.7 Ma coincident with the first evidence for the Acheulean stone tool technology.

scholarly journals Modelling the effects of CO2 on C3 and C4 grass competition during the mid-Pleistocene transition in South Africa

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Michaela Ecker ◽  
Douglas Kelley ◽  
Hiromitsu Sato
Keyword(s):  
South Africa ◽  
Water Availability ◽  
Climate Model ◽  
Fire Regimes ◽  
Early Pleistocene ◽  
Vegetation Model ◽  
Proxy Record ◽  
Climate Reconstructions ◽  
General Climate ◽  
Different Levels

Abstract Palaeoenvironmental reconstructions of the interior of South Africa show a wetter environment than today and a non-analogous vegetation structure in the Early Pleistocene. This includes the presence of grasses following both C3 and C4 photosynthetic pathways, whereas C3 grasses decline after the mid-Pleistocene transition (MPT, c. 1.2–0.8 Ma). However, the local terrestrial proxy record cannot distinguish between the potential drivers of these vegetation changes. In this study we show that low glacial CO2 levels, similar to those at the MPT, lead to the local decline of C3 grasses under conditions of decreased water availability, using a vegetation model (LPX) driven by Atmosphere–Ocean coupled General Climate Model climate reconstructions. We modelled vegetation for glacial climates under different levels of CO2 and fire regimes and find evidence that a combination of low CO2 and changed seasonality is driving the changes in grass cover, whereas fire has little influence on the ratio of C3:C4 grasses. Our results suggest the prevalence of a less vegetated landscape with limited, seasonal water availability, which could potentially explain the much sparser mid-Pleistocene archaeological record in the southern Kalahari.

Ground‐penetrating radar analysis of the Drimolen early Pleistocene fossil‐bearing palaeocave, South Africa

2021 ◽  
Author(s):  
Brian J. Armstrong ◽  
Stephanie Edwards‐Baker ◽  
Paul Penzo‐Kajewski ◽  
Andy I. R. Herries
Keyword(s):  
South Africa ◽  
Ground Penetrating Radar ◽  
Early Pleistocene ◽  
Ground Penetrating

scholarly journals Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

2021 ◽  
Vol 164 (3-4) ◽  
Author(s):  
Seshagiri Rao Kolusu ◽  
Christian Siderius ◽  
Martin C. Todd ◽  
Ajay Bhave ◽  
Declan Conway ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Investment Decisions ◽  
Climate Impacts ◽  
Future Climate ◽  
Eastern Africa ◽  
Climate Projections ◽  
Systematic Effect ◽  
Risk Profiles ◽  
Model Weighting

AbstractUncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa

2007 ◽  
Vol 68 (2) ◽  
pp. 271-297 ◽  
Author(s):  
K.J. Wessels ◽  
S.D. Prince ◽  
J. Malherbe ◽  
J. Small ◽  
P.E. Frost ◽  
...  
Keyword(s):  
South Africa ◽  
Land Degradation ◽  
Rainfall Variability

scholarly journals The Role of Mesoscale Convective Complexes in Southern Africa Summer Rainfall

2013 ◽  
Vol 26 (5) ◽  
pp. 1654-1668 ◽  
Author(s):  
R. C. Blamey ◽  
C. J. C. Reason
Keyword(s):  
South Africa ◽  
Southern Africa ◽  
Rainfall Variability ◽  
Warm Season ◽  
Tropical Rainfall Measuring Mission ◽  
Summer Rainfall ◽  
Spatial And Temporal Variability ◽  
Eastern Region ◽  
Southwest Indian Ocean ◽  
Mesoscale Convective

Abstract A combination of numerous factors, including geographic position, regional orography, and local sea surface temperatures, means that subtropical southern Africa experiences considerable spatial and temporal variability in rainfall and is prone to both frequent flooding and drought events. One system that may contribute to rainfall variability in the region is the mesoscale convective complex (MCC). In this study, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) data is used to document the precipitation produced by MCCs over southern Africa for the 1998–2006 period. Most of the rainfall associated with MCCs is found to occur over central Mozambique, extending southward to eastern South Africa. High precipitation totals associated with these systems also occur over the neighboring southwest Indian Ocean, particularly off the northeast coast of South Africa. MCCs are found to contribute up to 20% of the total summer rainfall (November–March) in parts of the eastern region of southern Africa. If the month of March is excluded from the analysis, then the contribution increases up to 24%. In general, the MCC summer rainfall contribution for most of the eastern region is approximately between 8% and 16%. Over the western interior and Botswana and Namibia, the MCC contribution is much less (<6%). It is also evident that there is considerable interannual variability associated with the contribution that these systems make to the total warm season rainfall.

scholarly journals South Asia river-flow projections and their implications for water resources

2015 ◽  
Vol 19 (12) ◽  
pp. 4783-4810 ◽  
Author(s):  
C. Mathison ◽  
A. J. Wiltshire ◽  
P. Falloon ◽  
A. J. Challinor
Keyword(s):  
Water Resources ◽  
South Asia ◽  
River Flow ◽  
Climate Model ◽  
Asian Summer Monsoon ◽  
Regional Climate ◽  
Global Climate Models ◽  
Climate Projections ◽  
The South ◽  
River Flows

Abstract. South Asia is a region with a large and rising population, a high dependence on water intense industries, such as agriculture and a highly variable climate. In recent years, fears over the changing Asian summer monsoon (ASM) and rapidly retreating glaciers together with increasing demands for water resources have caused concern over the reliability of water resources and the potential impact on intensely irrigated crops in this region. Despite these concerns, there is a lack of climate simulations with a high enough resolution to capture the complex orography, and water resource analysis is limited by a lack of observations of the water cycle for the region. In this paper we present the first 25 km resolution regional climate projections of river flow for the South Asia region. Two global climate models (GCMs), which represent the ASM reasonably well are downscaled (1960–2100) using a regional climate model (RCM). In the absence of robust observations, ERA-Interim reanalysis is also downscaled providing a constrained estimate of the water balance for the region for comparison against the GCMs (1990–2006). The RCM river flow is routed using a river-routing model to allow analysis of present-day and future river flows through comparison with available river gauge observations. We examine how useful these simulations are for understanding potential changes in water resources for the South Asia region. In general the downscaled GCMs capture the seasonality of the river flows but overestimate the maximum river flows compared to the observations probably due to a positive rainfall bias and a lack of abstraction in the model. The simulations suggest an increasing trend in annual mean river flows for some of the river gauges in this analysis, in some cases almost doubling by the end of the century. The future maximum river-flow rates still occur during the ASM period, with a magnitude in some cases, greater than the present-day natural variability. Increases in river flow could mean additional water resources for irrigation, the largest usage of water in this region, but has implications in terms of inundation risk. These projected increases could be more than countered by changes in demand due to depleted groundwater, increases in domestic use or expansion of water intense industries. Including missing hydrological processes in the model would make these projections more robust but could also change the sign of the projections.

scholarly journals Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

2017 ◽  
Vol 10 (3) ◽  
pp. 1383-1402 ◽  
Author(s):  
Paolo Davini ◽  
Jost von Hardenberg ◽  
Susanna Corti ◽  
Hannah M. Christensen ◽  
Stephan Juricke ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Rainfall Variability ◽  
Global Climate Model ◽  
Small Scale ◽  
Low Resolution ◽  
The Impact ◽  
Stochastic Physics

Abstract. The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979–2008) and a climate change projection (2039–2068), together with coupled transient runs (1850–2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of post-processed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate – specifically the Madden–Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with high-resolution simulations) or stochastically (in low-resolution simulations).

scholarly journals Using Empirical Orthogonal Teleconnections to evaluate interannual rainfall variability over China in the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations

2017 ◽  
Author(s):  
Claudia Christine Stephan ◽  
Nicholas P. Klingaman ◽  
Pier Luigi Vidale ◽  
Andrew G. Turner ◽  
Marie-Estelle Demory ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Rainfall Variability ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Unified Model ◽  
Seasonal Precipitation ◽  
Climate Simulations ◽  
Interannual Rainfall Variability ◽  
The Mean

Abstract. Six climate simulations of the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against observations and reanalysis data for their ability to simulate the mean state and year-to-year variability of precipitation over China. To analyze the sensitivity to air-sea coupling and horizontal resolution, atmosphere-only and coupled integrations at atmospheric horizontal resolutions of N96, N216 and N512 (corresponding to ~ 200, 90, and 40 km in the zonal direction at the equator, respectively) are analyzed. The mean and interannual variance of seasonal precipitation are too high in all simulations over China, but improve with finer resolution and coupling. Empirical Orthogonal Teleconnection (EOT) analysis is applied to simulated and observed precipitation to identify spatial patterns of temporally coherent interannual variability in seasonal precipitation. To connect these patterns to large-scale atmospheric and coupled air-sea processes, atmospheric and oceanic fields are regressed onto the corresponding seasonal-mean timeseries. All simulations reproduce the observed leading pattern of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are the four leading patterns associated with the observed physical mechanisms. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. However, finer resolution does not improve the fidelity of these patterns or their associated mechanisms. This shows that evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient. The EOT analysis adds knowledge about coherent variability and associated mechanisms.

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